{"id":"https://openalex.org/W146470039","doi":"https://doi.org/10.1007/s11042-010-0702-0","title":"Determination of emotional content of video clips by low-level audiovisual features","display_name":"Determination of emotional content of video clips by low-level audiovisual features","publication_year":2011,"publication_date":"2011-01-10","ids":{"openalex":"https://openalex.org/W146470039","doi":"https://doi.org/10.1007/s11042-010-0702-0","mag":"146470039"},"language":"en","primary_location":{"id":"doi:10.1007/s11042-010-0702-0","doi":"https://doi.org/10.1007/s11042-010-0702-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-010-0702-0","pdf_url":null,"source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/s11042-010-0702-0","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048563270","display_name":"Rene M A Teixeira","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ren\u00e9 Marcelino Abritta Teixeira","raw_affiliation_strings":["Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048624196","display_name":"Toshihiko Yamasaki","orcid":"https://orcid.org/0000-0002-1784-2314","observed_orcids":["https://orcid.org/0000-0002-1784-2314"]},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshihiko Yamasaki","raw_affiliation_strings":["Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069982192","display_name":"Kiyoharu Aizawa","orcid":"https://orcid.org/0000-0003-2146-6275","observed_orcids":["https://orcid.org/0000-0003-2146-6275"]},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kiyoharu Aizawa","raw_affiliation_strings":["Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan","Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5048563270"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":{"value":3190,"currency":"USD","value_usd":3190},"apc_paid":{"value":3190,"currency":"USD","value_usd":3190},"fwci":2.1009,"has_fulltext":false,"cited_by_count":43,"citation_normalized_percentile":{"value":0.88109858,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":99},"biblio":{"volume":"61","issue":"1","first_page":"21","last_page":"49"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.0031999999191612005,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.0006000000284984708,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/affective-model","display_name":"affective model","score":0.9330000281333923},{"id":"https://openalex.org/keywords/bayesian-networks","display_name":"Bayesian networks","score":0.7960000038146973},{"id":"https://openalex.org/keywords/hidden-markov-models","display_name":"hidden Markov models","score":0.921999990940094},{"id":"https://openalex.org/keywords/autoregressive-hidden-markov-model","display_name":"autoregressive hidden Markov model","score":0.9179999828338623},{"id":"https://openalex.org/keywords/decision-level-fusion","display_name":"decision fusion","score":0.6790000200271606},{"id":"https://openalex.org/keywords/early-fusion","display_name":"feature-level fusion","score":0.9950000047683716},{"id":"https://openalex.org/keywords/user-experiments","display_name":"user experiments","score":0.6510000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8121157288551331},{"id":"https://openalex.org/C2777113389","wikidata":"https://www.wikidata.org/wiki/Q208195","display_name":"Pleasure","level":2,"score":0.6064404249191284},{"id":"https://openalex.org/C2778598663","wikidata":"https://www.wikidata.org/wiki/Q1407599","display_name":"Video content analysis","level":4,"score":0.6038551926612854},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.5690585970878601},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.49929213523864746},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4894222319126129},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.47119662165641785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4553220570087433},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.4469015598297119},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4160594344139099},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3831257224082947},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3666282892227173},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.14515972137451172},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1411432921886444},{"id":"https://openalex.org/C65483669","wikidata":"https://www.wikidata.org/wiki/Q3536669","display_name":"Video processing","level":2,"score":0.10641878843307495},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11042-010-0702-0","doi":"https://doi.org/10.1007/s11042-010-0702-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-010-0702-0","pdf_url":null,"source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11042-010-0702-0","doi":"https://doi.org/10.1007/s11042-010-0702-0","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11042-010-0702-0","pdf_url":null,"source":{"id":"https://openalex.org/S110206669","display_name":"Multimedia Tools and Applications","issn_l":"1380-7501","issn":["1380-7501","1573-7721"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","jufo-1","ki-jl-1","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multimedia Tools and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W44741455","https://openalex.org/W132858152","https://openalex.org/W1555402191","https://openalex.org/W1615813862","https://openalex.org/W1793953714","https://openalex.org/W1837618112","https://openalex.org/W1966797434","https://openalex.org/W1976044816","https://openalex.org/W1977137834","https://openalex.org/W1984657827","https://openalex.org/W2001700175","https://openalex.org/W2003653478","https://openalex.org/W2011664673","https://openalex.org/W2042732045","https://openalex.org/W2043181832","https://openalex.org/W2069736034","https://openalex.org/W2075953807","https://openalex.org/W2081406015","https://openalex.org/W2083837083","https://openalex.org/W2099270617","https://openalex.org/W2099283244","https://openalex.org/W2105552115","https://openalex.org/W2106503382","https://openalex.org/W2106910805","https://openalex.org/W2107174446","https://openalex.org/W2110575115","https://openalex.org/W2114025269","https://openalex.org/W2114143824","https://openalex.org/W2128272608","https://openalex.org/W2141196583","https://openalex.org/W2142653084","https://openalex.org/W2143326392","https://openalex.org/W2143350951","https://openalex.org/W2143969506","https://openalex.org/W2148600927","https://openalex.org/W2156503193","https://openalex.org/W2156709807","https://openalex.org/W2157845099","https://openalex.org/W2167557160","https://openalex.org/W2171848217","https://openalex.org/W2291663305","https://openalex.org/W2901120339","https://openalex.org/W3171077326","https://openalex.org/W4240980853","https://openalex.org/W4243801427","https://openalex.org/W4251591997"],"related_works":["https://openalex.org/W2084807288","https://openalex.org/W2560940999","https://openalex.org/W2738341576","https://openalex.org/W2790495655","https://openalex.org/W2114025269","https://openalex.org/W306648316","https://openalex.org/W2147775340","https://openalex.org/W2082828292","https://openalex.org/W1525931151","https://openalex.org/W3005840512"],"abstract_inverted_index":{"Affective":[0],"analysis":[1],"of":[2,10,21,34,41,63,69,74,89,92,109,117,132,144,211],"video":[3,75],"content":[4],"has":[5],"greatly":[6],"increased":[7],"the":[8,11,35,39,61,67,114,130,141,226],"possibilities":[9],"way":[12],"we":[13,59],"perceive":[14],"and":[15,45,95,135,161,176,190,204,214,221,233],"deal":[16],"with":[17,53],"media.":[18],"Different":[19],"kinds":[20],"strategies":[22],"have":[23],"been":[24],"tried,":[25],"but":[26],"results":[27,62],"are":[28,155,207],"still":[29],"opened":[30],"to":[31,51],"improvements.":[32],"Most":[33],"problems":[36],"come":[37],"from":[38,102,125],"lack":[40],"standardized":[42],"test":[43],"set":[44],"real":[46,103],"affective":[47,70,82,98,115,146,200,217,219],"models.":[48,178],"In":[49],"order":[50],"cope":[52],"these":[54],"issues,":[55],"in":[56,209],"this":[57,149],"paper":[58],"describe":[60],"our":[64],"work":[65],"on":[66],"determination":[68],"models":[71,83,99,175],"for":[72,216],"evaluation":[73],"clips":[76],"using":[77,171,182,198],"audiovisual":[78],"low-level":[79],"features.":[80],"The":[81,97,167],"were":[84,100,169,196],"developed":[85],"following":[86],"two":[87,151,183],"classes":[88],"psychological":[90],"theories":[91],"affect:":[93],"categorial":[94,203],"dimensional.":[96,205],"created":[101],"data,":[104],"acquired":[105],"through":[106],"a":[107,118,122,126,157,186],"series":[108],"user":[110],"experiments.":[111],"They":[112],"reflect":[113],"state":[116],"viewer":[119],"after":[120],"watching":[121],"certain":[123],"scene":[124],"movie.":[127],"We":[128],"evaluate":[129],"detection":[131,142,212],"Pleasure,":[133],"Arousal":[134,224],"Dominance":[136],"coefficients":[137],"as":[138,140],"well":[139],"rate":[143,213],"six":[145],"categories.":[147],"For":[148],"end,":[150],"Bayesian":[152],"network":[153],"topologies":[154],"used,":[156],"Hidden":[158,164],"Markov":[159,165],"Model":[160],"an":[162],"Autoregressive":[163],"Model.":[166],"measurements":[168],"done":[170,181],"audio-only":[172],"models,":[173,201],"video-only":[174],"fused":[177],"Fusion":[179,189],"is":[180],"different":[184],"methods,":[185],"Decision":[187],"Level":[188,192],"Feature":[191],"Fusion.":[193],"All":[194],"tests":[195],"conducted":[197],"localized":[199],"both":[202],"Results":[206],"presented":[208],"terms":[210],"accuracy":[215],"families,":[218],"dimensions":[220],"probabilistic":[222],"networks.":[223],"was":[225],"best":[227],"detected":[228],"dimension,":[229],"followed":[230],"by":[231],"dominance":[232],"pleasure.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":8},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}